Cleaning Up Dirty Data with SSIS
Dirty data is everywhere, and it's headed for a database near you. Extraction, transformation, and loading (ETL) can be difficult, but often the most challenging component of that process is the validation and clean up of data. Information must be cleansed in such a way that it retains its original message and business value, while conforming to the expectations of the destination system(s).
In this session, we'll discuss some design patterns for addressing different types of dirty data using SQL Server Integration Services. We will review the various cleansing tools accessible from within SSIS including native Integration Services components, T-SQL, and SSIS scripting. In addition, we'll briefly review the new SQL Server Data Quality Services and its integration with SSIS. We'll cap off the discussion with demonstrations of several methods for data cleansing.
Tim Mitchell is a business intelligence consultant, author, and trainer. He has worked with SQL Server for over a decade, specializing in business intelligence, ETL/SSIS, data quality, and reporting. He holds a Bachelor’s Degree in Computer Science from Texas A&M at Commerce, and is recognized as aMicrosoft Data Platform MVP. Tim is an independent business intelligence consultant and a partner with Linchpin People, and is the principal at Tyleris Data Solutions.
As an active member of the community, Tim has spoken at international, regional, and local venues including the SQL PASS Summit, SQLBits, SQL Connections, SQL Saturday events, and various user groups and webcasts. Tim is coauthor of the book SSIS Design Patterns, and is a contributing author on the charity book project MVP Deep Dives 2. He is an active group member and speaker at the North Texas SQL Server User Group in the Dallas area.
You can visit his website and blog at TimMitchell.net or follow him on Twitter at twitter.com/Tim_Mitchell.